import gradio as gr
import numpy as np
import cv2
from core_pipeline import extract_frames, detect_trees, plot_detections

def process_video(video_file):
    if video_file is None:
        return None

    video_path = video_file.name  # ✅ fix for NamedString input
    frames = extract_frames(video_path)
    results = []

    for i, frame in enumerate(frames[:3]):  # Show top 3 sample frames
        detected, bboxes, confs, labels = detect_trees(frame)
        annotated = plot_detections(detected, bboxes)
        results.append(annotated)

    if results:
        preview = np.hstack(results)
        return preview
    return None

gr.Interface(
    fn=process_video,
    inputs=gr.File(label="Upload Drone Video", file_types=[".mp4"]),
    outputs=gr.Image(label="Tree Detections (Sample Frames)"),
    title="🌳 Drone Tree Detection App",
    description="Upload top-down drone footage (.mp4). This app detects trees using YOLOv8 and shows sample frames with bounding boxes."
).launch()